Dams are among the most important infrastructures in modern as well as developing countries. This importance is credited mainly due to their key roles in hydroelectric power generation and management of water resources for such diverse purposes as irrigation, water purification, and flood prevention. Thus, ensuring dam safety is of particular concern from economical, life-safety, and environmental viewpoints.
The Hydrostatic-Season-Time (HST) model used to model dam behaviour was first proposed in 1958 by “Electricité de France” for modeling pendulum behavior for the purpose of interpreting concrete dam movements. The HST model is the standard conventional method for modeling dam movements and is currently used by many dam owners around the globe. It is a statistical modeling technique based on multi-linear regression analysis using the historical data of the dam. Among various factors that affect dam structure deformation, HST considers the Hydrostatic pressure (H) (due to reservoir elevation), Season (S) and Time (T) as the basic cause or drive inputs while the displacement at a point in the dam measured by pendulums (plumb-lines) is the effect variable.
HST models can be used for validating future data and separate the effects of the above-mentioned inputs on the output variable. However, it has many limitations. For example, instead of considering the temperature variations explicitly, it approximates the actual air temperature variations with the manufactured virtual season (S) variable. Also, it does not incorporate into its inputs the informative data of other control instruments such as concrete temperature that affect the dam displacement. Furthermore, being a static model, HST is unable to capture the dynamics represented by lag times between cause-effect variables.
Therefore, there is a need to provide an improved solution to monitor and analyse dam health that will produce more accurate results.